(Note the connections)
CargoMetrics Cracks the Code on Shipping Data
Scott Borgerson and his team of quants at hedge fund firm CargoMetrics are using satellite intel on ships to identify mispriced securities.
By Fred R. Bleakley February 04, 2016
Link to article One late afternoon last November, as a ping-pong game echoed through the floor at CargoMetrics Technologies’ Boston office, CEO Scott Borgerson was watching over the shoulder of Arturo Ramos, who’s responsible for developing investment strategies with astrophysicist Ronnie Hoogerwerf. At Ramos’s feet sat Helios, his brindle pit-bull-and-greyhound mix. All three men were staring at a computer screen, tracking satellite signals from oil tankers sailing through the Strait of Malacca, the choke point between the Indian Ocean and the South China Sea where 40 percent of the world’s cargo trade moves by ship.
CargoMetrics, a start-up investment firm, is not your typical money manager or hedge fund. It was originally set up to supply information on cargo shipping to commodities traders, among others. Now it links satellite signals, historical shipping data and proprietary analytics for its own trading in commodities, currencies and equity index futures. There was an air of excitement in the office that day because the signals were continuing to show a slowdown in shipping that had earlier triggered the firm’s automated trading system to short West Texas Intermediate (WTI) oil futures. Two days later the U.S. Department of Energy’s official report came out, confirming the firm’s hunch, and the oil futures market reacted accordingly.
“We nailed it for our biggest return of the year,” says Borgerson, who had reason to breathe more easily. His backers were watching closely. They include Blackstone Alternative Asset Management (BAAM), the world’s largest hedge fund allocator, and seven wealthy tech and business leaders. Among them: former Lotus Development Corp. CEO Jim Manzi, who also had a long career at IBM Corp.
Compelling these investors and Borgerson to pursue the shipping slice of the economy is the simple fact that in this era of globalization 50,000 ships carry 90 percent of the $18.5 trillion in annual world trade.
That’s no secret, of course, but Borgerson and CargoMetrics’ backers maintain that the firm is well ahead of any other investment manager in harnessing such information for a potential big advantage. It’s why Borgerson has kept the firm in stealth mode for years. In its earlier iteration, from 2011 to 2014, CargoMetrics was hidden in a back alley, above a restaurant. Now that he’s running an investment firm, Borgerson declines to name his investors unless, like Manzi and BAAM, they are willing to be identified.
“My vision is to map historically and in real time what’s really going on in economic supply and demand across the planet,” says the U.S. Coast Guard veteran, who prides himself and the CargoMetrics team on not being prototypical Wall Streeters. “The problem is enormous, but the potential reward is huge.”
According to Borgerson, CargoMetrics is building a “learning machine” that will be able to automatically profit from spotting any publicly traded security that is mispriced, using what he refers to as systematic fundamental macro strategies. He calls the firm a new breed of quantitative investment manager. In unguarded moments he sees himself as the Steve Jobs or Elon Musk of portfolio management.
Though his ambitions may sound audacious, one thing is certain: Borgerson doesn’t lack in self-confidence. Over the past six years, he has secretly and painstakingly built a firm heavy in Ph.D.s that can manage a database of hundreds of billions of historical shipping records, conduct trillions of calculations on hundreds of computer servers and systematically execute trades in 28 different commodities and currencies.
For his part, Borgerson seems an unlikely architect of such a serious, ambitious endeavor. Easygoing and fond of joking with his colleagues, he is a hands-off manager who credits CargoMetrics’ investment prowess to his team. His brand of humor comes through even when he’s detailing the series of challenges he had starting the firm. After using the phrase “It was hard” several times, he pauses and adds, “Did I mention it was hard?” Although Borgerson declines to provide any specifics about CargoMetrics’ portfolio, citing the advice of his lawyers, performance during the three years of live trading apparently has been strong enough to keep his backers confident and his team of physicists, software engineers and mathematicians in place. “Hopefully, it won’t be too long before we can make a more significant investment,” says BAAM CEO J. Tomilson Hill. Former Lotus CEO Manzi is optimistic about the firm’s prospects: “It has an unbelievable edge with its historical data.”
CargoMetrics was one of the first maritime data analytics companies to seize the potential of the global Automatic Identification System. Ships transmit AIS signals via very high frequency (VHF) radio to receiver devices on other ships or land. Since 2004, large vessels with gross tonnage of 300 or more are required to flash AIS positioning signals every few seconds to avoid collisions. That allows CargoMetrics to pay satellite companies for access to the signals gleaned from 500 miles above the water. The firm uses historical data to identify cargo and aggregation of cargo flow, and then applies sophisticated analysis of financial market correlations to identify buying and selling opportunities.
“We’re big-data junkies who could not have founded CargoMetrics without the radical breakthroughs of this golden age of technology,” Borgerson says. The revolution in cloud computing has been instrumental. CargoMetrics leverages the Amazon Web Services platform to run its analytics and algorithms on hundreds of computer servers at a fraction of the cost of owning and maintaining the hardware itself.
At his firm’s headquarters — where the lobby displays a series of colored semaphore signal flags that spell out the mathematical equation for the surface area of the earth —Borgerson leads the way to his server room. It’s the size of a closet; inside, a thick pipe carries all the data traffic and analytic formulas CargoMetrics needs. That computing power alone would have cost $30 million to $40 million, Manzi says.
CargoMetrics is pursuing a modern version of an age-old quest. Think of the Rothschild family’s use in the 19th century of carrier pigeons and couriers on horseback to bring news from the Napoleonic Wars to their traders in London, or, in the 1980s, oil trader Marc Rich’s use of satellite phones and binoculars for relaying oil tanker flow.
Other quant-focused Wall Street firms are latching onto the satellite ship-tracking data. But, Borgerson says, “I would bet my life on a stack of Bibles that no one in the world has the shipping database and analytics we have.” The reason he’s so convinced is that from late 2008 he was an early client of the satellite companies that had begun collecting data received from space and on land to build a large database of all the world’s vessel movements in one place.
That’s what caught Hill’s eye at Blackstone when he learned of CargoMetrics a few years ago. BAAM now has a managed account with the firm. “If anyone else tries to replicate what CargoMetrics has, they will be years behind [Borgerson] on data analytics,” Hill says. “We know that a number of hedge fund data scientists want his data.”
But too much reliance on big data can go wrong, say many academicians. “There is a huge amount of hype around big data,” observes Willy Shih, a professor of management practice at Harvard Business School. “Many people are saying, ‘Let the data speak; we don’t need theory or modeling.’ I argue that even with using new, massively parallel computing systems for modeling and simulation, some forces in nature and the economy are still too big and complex for computers to handle.”
Shih’s skepticism doesn’t go as far as to say the data challenge on global trade is too big a puzzle to solve. When informed of the CargoMetrics approach, he called it “very valid and creative. They just have to be careful not to throw away efforts to understand causality.”
Another big-data scholar, Massachusetts Institute of Technology professor of electrical engineering and computer science Samuel Madden, also urges caution. “What worries me is that models become trusted but then fail,” he explains. “You have to validate and revalidate.”
Borgerson grew up in Southeast Missouri, in a home on Rural Route 5 between Festus and Hematite. His father was a former Marine infantry officer and police official, and his mother a high school French and Spanish teacher. The family traveled 15 miles to Crystal City to attend Grace Presbyterian Church, which was central to young Borgerson’s upbringing: There he was a youth elder, became an Eagle Scout and received a God and Country Award. The church was across the street from the former home of NBA all-star and U.S. senator Bill Bradley, whose backboard Borgerson used for basketball practice.
When it came to choosing what to do after high school, Borgerson was torn between becoming a Presbyterian minister and accepting an appointment to the U.S. Coast Guard Academy or West Point. He went with the Coast Guard because, he says, “the humanitarian mission really appealed to me, and I had never been on a boat before.”
At the academy, in New London, Connecticut, Borgerson played NCAA tennis and was also a cutup, racking up demerits for such antics as placing a sailboat on the commandant of cadets’ front lawn and leading bar patrons in a rendition of “Semper Paratus,” the school’s theme song. Still, he graduated with honors and spent the next four years piloting a 367-foot cutter — which seized five tons of cocaine in the Caribbean — then captaining a patrol boat that saved 30 lives on search-and-rescue missions. From 2001 to 2003 the Coast Guard sent Borgerson to the Fletcher School at Tufts University to earn his master’s of arts in law and diplomacy. While at Tufts he volunteered at a Boston homeless shelter for military veterans and founded a Pet Pals therapy program for senior citizens.
Following graduation, from 2003 to 2006, Borgerson taught U.S. history, foreign policy, political geography and maritime studies at the Coast Guard Academy, and co-founded its Institute for Leadership. While there he would get up at 4:00 each morning to work on his Ph.D. thesis exploring U.S. port cities’ approaches to foreign policy. He would also travel to Boston to complete his course work at Tufts and meet with his adviser, John Curtis Perry.
Borgerson’s military allegiance runs deep. One weekend last fall he played football in a service academy alumni game. On another he attended the Army-Navy game. Still militarily fit at age 40, the 6-foot-5 Borgerson works out regularly at an inner-city gym aimed at helping youths find an alternative to gang violence; a few weeks ago he was there boxing with ex-convicts and lifting weights.
Leaving the Coast Guard was a hard decision for Borgerson, resulting in part from his frustration with the military bureaucracy’s stymieing of his bid to get back to sea for security missions. With his degrees in hand, he applied for a fellowship at the Council on Foreign Relations. During the application process he met Edward Morse, now global head of commodities research at Citigroup. Morse was on the CFR selection committee in 2007 and recommended Borgerson as a fellow.
Morse introduced Borgerson to commodities, and to trading terms like “contango” and “backwardation.” Morse himself had, earlier in career, gotten the jump on official oil supply data by hiring planes to take photos of the lid heights of oil tanks in Oklahoma’s Cushing field.
Working for the CFR in New York reconnected Borgerson with his Missouri roots. Bill Bradley’s aunt called the former senator to say: “The son of a family who went to our church in Crystal City is in New York. Would you welcome him?” Bradley did — and would later play a part in Borgerson’s career development.
While at the CFR, Borgerson became an expert on the melting of the North Pole ice cap, writing numerous published articles on its implications; this led him to co-found, with the president of Iceland, the Arctic Circle, a nonprofit designed to encourage discussion of the future of that region. Borgerson recently spoke to 50 international generals and admirals about the Arctic and is co-drafting a proposal for a treaty between the U.S. and Canada that would help resolve the differences the two countries have in allowing international ship and aircraft travel through the Northwest Passage.
His Arctic research led to an aha moment early in 2008, while he was still with the CFR, on a visit to Singapore and the Strait of Malacca with his Fletcher School classmate Rockford Weitz and their former Ph.D. adviser, Perry. Seeing the mass of ships sailing through the strait, Borgerson and Weitz decided to build a data analytics firm using satellite tracking of ships.
Like many successful entrepreneurs, the two struggled to find financing before reaching out to a network of friends and their contacts. One was Randy Beardsworth, who had sat with Borgerson at a 2007 Coast Guard Academy dinner, where Beardsworth, then the Coast Guard’s chief of law enforcement in Miami, was the guest speaker. Borgerson “made references to history and literature, and I thought, ‘Here is a sharp guy,’” recalls Beardsworth. “We have been friends ever since.”
But Borgerson didn’t turn to his new friend in his initial fund-raising. “He came to me in 2009, after he had been turned down by 17 VCs, was maxed out on his credit card, was married and had a newborn son,” says Beardsworth, who was reviewing the Department of Homeland Security as part of the Obama administration’s transition team. Beardsworth came to the rescue, not only committing to invest a small amount but introducing his friend to Doug Doan. A West Point graduate and Washington-based angel investor, Doan took to Borgerson right away. “To be honest, it wasn’t his idea, it was Scott I invested in,” says Doan, who provided $100,000 in capital and introduced Borgerson to a few friends, who added $75,000. Manzi came on board as an investor in 2009, having been asked by Bradley to check out Borgerson’s plan for a data metrics firm. (Manzi knew Bradley from the late 1990s, when the latter was considering a run for U.S. president.)
With Doan, Doan’s friends and Manzi as investors, CargoMetrics was finally able to garner its first venture capital commitment in early 2010, from Boston-based Ascent Venture Partners. That gave the start-up the capital it needed to hire a bevy of data scientists to build an analytics platform that it could sell to commodity-trading houses and other commercial users. In 2011, CargoMetrics added Summerhill Venture Partners, a Toronto-based firm with a Boston office, to its investor roster, raising roughly $18 million from venture capital and angels for its data business.
By then Borgerson had already begun to contemplate converting CargoMetrics from an information provider into a money manager; he saw the potential to extract powerful trade signals from its technology rather than share it with other market participants for a fee. Among those he consulted was serial entrepreneur Peter Platzer, a friend of one of CargoMetrics’ original investors. Platzer, a physicist by training, had spent eight years as a quantitative hedge fund manager at Rohatyn Group and Deutsche Bank before co-founding Spire Global, a San Francisco–based company that uses its own fleet of low-orbit satellites to track shipping, in 2012. “We had lengthy conversations on how to set up quant trading systems and how [commodities giant] Cargill had made a similar decision to set up its own in-house hedge fund to trade on the information it was gathering,” recalls Platzer. So Borgerson reset his course. Doan describes the decision as a “transformative moment” for the CargoMetrics co-founder. “The military trains you to be a strategic thinker,” Doan explains. “Scott had been tactical until then, making small pivots, and like a general who sees the theater of war, he moved into strategic mode.”
Borgerson’s ambition to succeed was in no small part fueled by the early turndowns by many venture capital firms and a fierce determination to best the Wall Street bunch at their own game. “There’s a lot that motivates me, including — if I’m honest — I have a big chip on my shoulder to beat the prep school, Ivy League, MBA crowd,” he says. “They’re bred to make money, but they’re not smarter than everyone else; they just have more patina and connections.” (Bred differently, he spent last Thanksgiving visiting his parents in rural Missouri. After breakfast he and his father were in the woods, shooting assault guns at posters of terrorists, with Gunny, his father’s Anatolian shepherd dog.)
Borgerson’s plan was not met with enthusiasm from the company’s then co-CEO, Weitz. CargoMetrics had been gaining clients and meeting its goals, and was on its way to becoming a successful data service provider. Weitz, who now is president of the Gloucester, Massachusetts–based Institute for Global Maritime Studies and an entrepreneur coach at Tufts’ Fletcher School, did not return e-mails or phone calls asking for comment. For his part, Borgerson says: “A ship cannot have two captains. The company simply matured and evolved into a streamlined management structure with one CEO instead of two.”
Eventually, Doan went along with Borgerson’s plan. “We believe in Scott and that shipping holds the no-shit, honest truth of what the economy is doing,” he says. But buying out the venture capital firms several years ahead of the usual exit time would require a hefty premium over what they had invested.
Once again Borgerson’s early supporters played a key role. Manzi, a fellow Fletcher School grad who had mentored Borgerson since the company’s early days, put up more money (making CargoMetrics one of his single largest investments) and introduced him to a powerful group of wealthy investors. Separately, the CFR’s Morse suggested that Borgerson meet with Daniel Freifeld, founder of Washington-based Callaway Capital Management and a former senior adviser on Eurasian energy at the U.S. Department of State. Impressed by Borgerson’s “intellectual honesty, vigor and more than four years of historical data,” Freifeld brought the idea to a billionaire third-party investor, who took his advice and became one of CargoMetrics’ largest backers. “I would not have suggested the investment if CargoMetrics had not done the hard part first,” adds Freifeld, declining to name the investor.
A chance encounter in the fall of 2012 gave the CargoMetrics team its first taste of real Wall Street trading. Attending an Arctic Imperative conference in Alaska, Borgerson met the CIO of a large investment firm, whom he declines to name. When Borgerson confided his ambition and that CargoMetrics had developed algorithms to trade on its shipping data once it was legally structured to do so, the CIO suggested CargoMetrics provide the analytical models for a separate portfolio the money manager would trade. Live trading using CargoMetrics’ models began in December 2012. Manzi brought in longtime banker Gerald Rosenfeld in 2013 to craft and negotiate the move to make CargoMetrics a limited liability investment firm. Rosenfeld acted in a personal role rather than in his position as vice chairman of Lazard and full-time professor and trustee of the New York University School of Law. The whole process took a year and a half. During that time Blackstone checked in as an investor.
Bradley, now an investment banker, has yet to invest in CargoMetrics, explaining that he is unfamiliar with quantitative investing. But he may eventually invest in Borgerson’s firm, he says, because “we are homeboys. I believe in him and that things are going to work out ” — pausing to add with a smile, “based on my vast quant experience, of course.”
Borgerson has been in stealth mode since CargoMetrics’ early days, when he moved the firm from an innovation lab near MIT because the shared space was too open. He is much more forthcoming when boasting of the firm’s “world-class talent.” The team includes astrophysicists, mathematicians, former hedge fund quants, electrical engineers, a trade lawyer and software developers. Hoogerwerf, who has a Ph.D. in astrophysics from the Netherlands’ Leiden University, built distributed technical environments for scientists and engineers at Microsoft Corp. Solomon Todesse, who works on quant investment strategies, was head of asset allocation at State Street Global Advisors. Aquil Abdullah, a team leader in the engineering group, was a software engineer in the high-performance-computing group at Microsoft. And senior investment strategist Charles Freifeld (Daniel’s father) has 40 years’ experience in futures and commodities markets, including nine with Boston-based commodity trading adviser firm AlphaMetrics Capital Management.
“All were self-made people; none were born with a silver spoon,” Borgerson notes. One of his blue-collar-background hires was James (Jess) Scully, who joined as chief operating officer in 2011, after his employer Interactive Supercomputing was acquired by Microsoft.
“The team we built treasures team success, which is Scott’s motto,” Scully says. “We want shared resources, one P&L, not ‘How much money did my unit make?’” Both Scully and Borgerson say CargoMetrics is like the Golden State Warriors, a leading NBA basketball team known for putting aside personal glory and playing as a band of brothers having fun.
Borgerson says he fosters a no-ego policy with “lots of play because investment teams are built on trust, and playing together builds trust.” Team building at CargoMetrics includes pub crawls, picnics at Borgerson’s house, poker nights, volunteer work in a soup kitchen for the homeless, Red Sox games and visits to museums.
Trips to the Boston docks or Coast Guard base are intended to remind the CargoMetrics team of the real economy. There are also occasional “touch a tanker” days. On one visit to a tanker, everyone was amazed that the ship was the size of a city building, Borgerson says. “They could smell the salt on the deck,” he recalls. “Wall Street can lose sight of the real fundamentals in the world. I don’t want that to happen here.”
Unlike the Rothschilds 200 years ago, only a small percentage of the trades that CargoMetrics makes relate to beating official government data. Most simply are aimed at identifying mispricings in the market, using the firm’s real-time shipping data and proprietary algorithms.
At a whiteboard in his conference room, Borgerson sketches out CargoMetrics’ general formula. He draws a “maritime matrix” of three dynamic data sets: geography (Malacca, Brazil, Australia, China, Europe and the U.S.), metrics (ship counts, cargo mass and volume, ship speed and port congestion) and tradable factors (Brent crude versus WTI, as well as mining equities, commodity macro and Asian economic activity). Using satellite data with hundreds of millions of ship positions, CargoMetrics makes trillions of calculations to determine individual cargoes onboard the ships and then to aggregate the cargo flows and compare them with historical shipping data. All that leads to the final comparisons with historical financial market data to find mispricings. If CargoMetrics observes an appreciable decline in export shipping activity in South Africa, for example, its trading models will determine whether that is a significant early-warning sign by considering that information alongside other factors, such as interest rates. If CargoMetrics believes a decline in the rand is forthcoming, it might short it against a basket of other currencies. “This is like a heat map showing opportunity,” Borgerson says, noting that CargoMetrics is not trading physical commodities. “We are agnostic on whether to be long or short, and let the computers spot where there is a mispricing and liquidity in the markets.” He sums up his simple, but still less than revealing, process by writing on the whiteboard “Collect, Compute, Trade.”
Borgerson says CargoMetrics is building a systematic approach that will work even when cargo cannot be identified — on containerships, for instance. It already knows a large percentage of the daily imports and exports into and out of China and island economies such as Japan and Australia. And although the firm cannot glean from its calculations on satellite AIS data the type of cargo, such as iPhones from China, it can measure total flow, which shows present economic activity. CargoMetrics’ data scientists are working on linking such activity to the firm’s data set of the past seven years to measure the evolving global economy. That will lead, Borgerson maintains, to more trades on currencies and equity index futures and, eventually, trades on individual equities. “Uncorrelated” is a mantra of Borgerson and his team. Well aware that correlated assets sent the performance of most asset managers, including hedge funds, plunging in the financial crisis, CargoMetrics is determined to come up with an antidote. Careful not to say too much, Borgerson lays out the simple principle that the process starts with placing many bets among uncorrelated strategies in different asset classes, like commodities, currencies and equities.
The goal is diversification, staying as market neutral as possible and remaining sensitive to tail risk in different scenarios. CargoMetrics’ analytic models help find asset classes that are outliers. Those may include a publicly traded instrument such as oil, another commodity or an equity for which shipping information was a leading indicator during times when other asset classes marched in lockstep. The historical ship data is then blended with this new information to seek opportunities. Identifying mispriced spreads among different trades within an asset class is another way of avoiding the calamity of correlation. Borgerson says the firm’s models will find instances where one type of oil should be a short trade and another a long one. The same goes for whole asset classes — shorting one that will benefit if virtually all asset prices plunge and buying another that will rise when oil prices gain. “We’re counting cards with the goal of being right maybe 3 percent more than we are wrong, as a way of making profits during good times and staying afloat during times of sudden, unpredictable but far-reaching events,” Borgerson says. The key, he adds, “is to know your edge and spread your risk.” CargoMetrics’ uncorrelated approach worked during the dismal first three weeks of this year, says Borgerson. Dialing down risk as volatility in the markets soared, the firm was on track in January to have its best month since it began trading.
To improve the firm’s models, eight of its data scientists hold a weekly strategy meeting, nicknamed “the Shackleton Group” after the band of sailors shipwrecked in the Antarctic from 1914 to 1917. Hoogerwerf and Ramos co-lead the group. At one recent meeting they were deciding how much risk, including how much liquidity, there was in a possible strategy; reviewing whether to keep previous strategies; and assigning who would research new ones.
The Shackleton Group’s meetings are free-form, with a lot of “I’ve got an idea” interjections that disregard official roles. “We hit the restart button a lot,” says Ramos, a former director of business intelligence and a quantitative economist at law firm Dewey & LeBoeuf who joined CargoMetrics in late 2010. “That’s why our motto is ‘Never lose hope.’” A bet on oil, related to Russia’s production, was stopped at the last minute in 2014, when Russia invaded Ukraine. Some currency-trading strategies have been abandoned in theory or after failing. Strategies the Shackleton Group likes are passed on to the firm’s investment committee of Borgerson, Scully and Ramos for a final decision. CargoMetrics has a unique set of big-data challenges. Historical shipping patterns may not be as useful in the new global economy now that shipping freight prices are plunging, a sign that trade growth rates may be changing. And analysts point out how hard identifying oil cargo can be in certain locations and instances, even in more-predictable economic times. “While it may be easy to say that ships leaving the Middle East Gulf are typically carrying crude oil, knowing the type of crude is sometimes quite difficult,” says Paulo Nery, senior director of Europe, Middle East and Asia oil for Genscape, a Louisville, Kentucky–based company that analyzes satellite tracking of ships. Borgerson maintains his team is well aware of the dangers of data mining and getting swamped by noise. “If you run computers hard enough, you can convince yourself of anything,” he says. To make sure CargoMetrics’ algorithms for identifying cargo are valid, the firm spot-checks manifest data filed at ports and imposes statistical confidence checks to guard against spurious correlations.
Getting the jump on official government statistics is likely to become tougher too thanks to the recently formed High-Level Group for the Modernization of Official Statistics. Although the U.S. is not a member, Canada is a key player helping to lead the mostly European nation group (including South Korea) in coming up with a global blueprint for measuring and reporting economic activity.
Reflecting on his journey to Wall Street — raising money, hiring employees with different skill sets, making changes to CargoMetrics’ culture, overcoming legal and regulatory hurdles — almost gives Borgerson second thoughts about whether he would do it again. “I’ve sailed ships through tropical storms, captured cocaine smugglers and testified before Congress [on his Arctic research],” he says, “but this was the hardest.”
What's on your agenda for 2030? Have you made plans? Are you planning on making plans? Are your goals realistic? Are they inline with our approaching Not Normal New Normal online reality that the world wide WEB is going to
offer become? Because nobody will own anything, anymore, ever again. All things will be a part of the WEB. Most of us won’t have to work because we won't have to make anyTHING in the IoT (Internet ofThings).
An IoT system consists of sensors/devices which “talk” to the cloud through some kind of connectivity. Once the data gets to the cloud, software processes it and then might decide to perform an action, such as sending an alert or automatically adjusting the sensors/devices without the need for the user. Oct 29, 2016.
IoT makes everyday objects 'smart' by enabling them to transmit data and automate tasks, without requiring any manual intervention. Essentially, any object that can be connected to the internet and controlled that way is a candidate for an IoT device.
And who will run the WEB? AI. It will
capture trap data and make decisions about how to best apply that data. And we are data, and they have to analyze and interpret the data to know how to best use that data.
The benefits of IoT for apparel and accessories customers are huge, and most of them are linked to health. Smart sensors located in a shirt (see Hexoskin), for example, could track your heart-rate or temperature, while socks could measure your steps, calories consumed, amongst other data.
Ah. It's for our own good. Thank God. I was getting worried there for a second. Let's see how else it could be used for the betterment of society.
Along with advanced data analytics, IoT-enabled devices and sensors are helping us reduce air pollution in some of our world's biggest cities, improve agriculture and our food supply, and even detect and contain deadly viruses. Source Here
Reduce air pollution. Improve agriculture. And contain deadly viruses. Like now. See? If the IoT had been here soon enough, we wouldn't have to be living in isolation through a pandemic. This is why it's vitality important to implement certain measures that normally would be considered verboten, like a health pass.
Health Pass • The confidence to move forward. Health Pass by CLEAR gives employees and consumers the confidence and peace of mind to get back to work, shop at their favorite store, step into a restaurant and attend a ball game. For over 10 years, CLEAR has been the trusted industry leader for biometric identity and access. Now, no matter where you go, CLEAR's established platform can make everyday experiences easier and safer for everyone. Source Here
Are you
CLEAR? Because if you aren't, you're a danger to society and then society's way forward won't be
CLEAR. Society's future will remain in our distant past as we will have to remain socially distant, which will prevent the NOT NORMAL NEW NORMAL from becoming our BOT NORMAL NEW NORMAL. Just to be sure that we're all on the same page, there will be
GATES to keep us cordoned until we can be
CLEARed by an AI IoT system through the data in our clothes. And that's important, because we can't have Viruses running rampant and spreading, because a Virus is deadly when your living in an Information Based Economy. Do you have a
CLEARVIEW of where this is headed yet? I bet you do, but let's be sure.
Clearview.ai • Clearview AI is a new research tool used by law enforcement agencies to identify perpetrators and victims of crimes. Clearview AI's technology has helped law enforcement track down hundreds of at-large criminals, including pedophiles, terrorists and sex traffickers. It is also used to help exonerate the innocent and identify the victims of crimes including child sex abuse and financial fraud. Using Clearview AI, law enforcement is able to catch the most dangerous criminals, solve the toughest cold cases and make communities safer, especially the most vulnerable among us.
The most vulnerable among us? Who are they? Right here, right now? Let's think, shall we?
The COVID-19 pandemic is impacting the global population in drastic ways. In many countries, older people are facing the most threats and challenges at this time. Although all age groups are at risk of contracting COVID-19, older people face significant risk of developing severe illness if they contract the disease due to physiological changes that come with ageing and potential underlying health conditions.
Voila! I feel safer, do you feel safer? Of course you do! Let's put on our
face condoms masks and build a socially distant campfire and sing kumbaya and It's a
AI Small World After All. Because after all is said and done, we all become old one day. And nobody wants to die before they have to. And think of the elderly. All the sacrifices that they've made for the younger generations! After all, whose going to make our fast food?
If you look closely when you walk into your favorite fast food restaurant or casual dining restaurant, you might notice something different. Where there used to be waiters and hostesses who had just learned to drive, these jobs are now being filled by a completely different demographic – senior citizens.
Thank you seniors! You truly are the greatest generation that has ever lived. Holding the lines while burger bombs and french fry grenades explode around you! Without you, who would make sure we received our fast food, fast?
April 19, 2019 • And more businesses are taking note. In fact, according to Gartner, “by 2020, 85 percent of customer interactions will be managed without a human.” Fast-food companies have heavily invested in automation, analytics and artificial intelligence technologies in recent years, and it’s fair to expect the trend to continue and expand as AI grows increasingly advanced and becomes more accessible.And more businesses are taking note. In fact, according to Gartner, “by 2020, 85 percent of customer interactions will be managed without a human.” Fast-food companies have heavily invested in automation, analytics and artificial intelligence technologies in recent years, and it’s fair to expect the trend to continue and expand as AI grows increasingly advanced and becomes more accessible. Source Here
Raise the praise for Ronald McDonald's Happy Meals! Now we can isolate the elderly and they don't have to work anymore! After all, should they really be working at their age? Aren't they kind of old, and, uhm, well, not to be impolite or rude, but aren't they getting a bit senile to be employed?
Nancy Pelosi Glitches •
Source Here Hillary Clinton Stumbles at 911 event •
Source Here Awkward moment Donald Trump forgets to sign executive order •
Source Here Joe Biden forgets Kamala Harris is Black Woman senator •
Source Here Those politicians sure are
ACTING old, aren't they? Should they really be in charge of the most powerful nation on the planet? Maybe we should find an alternative, like an AI World Government •
Source Here Or maybe get a younger guy in there until AI is ready? Somebody like Andrew Yang. Somebody who wants UBI so that we don't starve when the robots take our jobs •
Source Here Health Pass to keep us alive. UBI to feed us. AI running the planet. Why, it almost looks like it was a well thought out plan, doesn't it? Like a chess game. Or a game of Go. So maybe we should jump into the
Deep Blue and just
AlphaGo and get a move on already? I can't speak for you, but I'm excited about not having a purpose in life. Sounds great, doesn't it? We can all be Comfortably Numb together!
Now. Do you have a
Clearview yet? Maybe? Possibly? Or maybe you're unsure? On the fence. Stuck in the middle? Allow me to elucidate some more to convince the undecided.
MAGA. Make America Great Again. Great slogan, right? It really whips up the patriotism when you think of all of those jobs being brought back from China. Jobs for you. Except.
Forrester predicts that by 2025, technologies like robots, artificial intelligence (AI), machine learning, and automation will replace 7% (or 22.7 million) jobs in the US alone. Source Here
Repetitive tasks like factory work. Or Trucking and Parcel Delivery.
Long-Haul Trucking • I think we will see significant numbers of self-driving trucks in the next five to 10 years, even before self-driving cars. There is a significant shortage of people willing to drive the big rigs down the highway and significant price pressure on their wages. Just think, wouldn’t it be great if all the trucks would stay in one lane? It would be a huge win for the shipping companies and for the general public.
Delivery Services • Automation is best when it is a repetitive action. Restaurants already use automation to dispense your soda in the drive-through or fry up a batch of fries when more are needed. I see package delivery being automated at some point. Drones are already being tested to deliver packages—why not a self-driving truck with a drone to drop the package off at the door? Source Here
That's. A lot. Of jobs. Isn't it? Yes it is. But surely governments are only looking out for us, right? Surely they aren't in league with tech corporations who have a hidden in plain sight agenda being executed under a digital canopy of camouflage?
June 20, 2017 • Donald Trump called for “sweeping transformation of the federal government’s technology” during the first meeting of the American Technology Council, established by executive order last month. Eighteen of America’s leading technology executives – including Amazon CEO Jeff Bezos, Apple CEO Tim Cook, Microsoft CEO Satya Nadella, and Eric Schmidt, the executive chairman of Google parent Alphabet – convened at the White House Monday for the summit. “Government needs to catch up with the technology revolution,” said Trump. “America should be the global leader in government technology just as we are in every other aspect, and we are going to start our big edge again in technology – such an important industry.” Source Here
I've spoken about this before: Big Tech and the Government is the same thing. Yes, yes, I know, they're gonna break them apart. Okie Dokie. Gotcha.
Google’s true origin partly lies in CIA and NSA research grants for mass surveillance •
Source Here Apple's SIRI • Apple’s digital virtual assistant started life as a DARPA project in the early 2000s, known as CALO – ostensibly an acronym for Cognitive Assistant that Learns and Organizes, but also a nod to the Latin word for a soldier’s servant.
SOURCE HERE Microsoft • THE MICROSOFT POLICE STATE: MASS SURVEILLANCE, FACIAL RECOGNITION, AND THE AZURE CLOUD • Microsoft helps police surveil and patrol communities through its own offerings and a network of partnerships — while its PR efforts obscure this.
Source Here Amazon • The Details About the CIA's Deal With Amazon •
Source Here Ok. They're pretty well in bed with each other. And with the IoT and 5G coming along, they'll be watching us in our beds, all the time. Here's hoping that we don't end up on Pornhub, right? Why it's almost like all that free porn is designed to make us used to the act of being recorded while we have sex. There are sure a lot of amateur recordings being uploaded, aren't there?
Let's look.
Categories viewed the longest in the U.S.—13 to 14 minutes: • Amateur (that is, not produced by commercial entities) Categories that gained the most views from 2016 to 2017: • Cuckold (men watching other men with their gals), up 72 percent. Source Here
Strange statistics, aren't they? They don't rank them up above, and there are others, so maybe they're blips. Aberrations? Not Normal.
The category with the greatest increase in traffic, 108 per cent, was 'amateur' • Source Here
Nope. That's a Not Normal New Normal statistic for sure. But we're not being conditioned to being watched in an internet of things exhibition, are we?
This is
coming (pardon the pun) and most people are asleep at the wheel. We're going to have a
hard (sorry again) time convincing anyone that this is real. A really hard time (ok, ill stop). You see, whatever groups have came up with this have been at it for a long (not another pun, I swear) time. It's designed to make our lives easier and better. On paper. But. How can we trust any of it? This is uncharted water. And it wouldn't be the first time the scientists and the politicians haven't had our best intentions. Heck, it wouldn't even be the first time that they did have good intentions and ended up screwing (not a punhub pun) it up.
The short and simple version of what were seeing carried out before our eyes is this; the Garden of Eden.
November 15, 1997 • The latest entrant to the utopian ranks seems to be Freeman J. Dyson, the 73-year-old mathematician and former physicist, who is now a futurologist at the Institute for Advanced Study in Princeton, N.J. Like the scientific utopians at this century's start, Mr. Dyson says technology provides the bridge to a heaven on earth. He conjures up a world where trees, not oil wells, produce fuel, where rural villages are the major source of wealth, and former slum dwellers are hooked up to the Internet. In his vision, new technologies pointed in the right direction could create such a poverty-free utopia, leapfrogging the dishearteningly slow efforts of the World Bank and other do-gooders to promote development. Source Here
Where rural villages are the major source of wealth? Hmmm.
Rich flee NYC, workers deal with COVID-19 •
Source Here Hollywood Apocalypse: The rich and famous are fleeing in droves •
Source Here That's quite the coincidence, isn't it?
What research into this holy trinity of solar energy, genetics and computers now needs, Mr. Dyson said, is ''a strong ethical push'' to get all three technologies working in tandem to create ''a socially just world.''
An ethical push. Like a Pandemic. Like now. So it's a great thing that the
Great Reset was ready to go and we can Build Back Better because We're All In This New Normal Together and You're Either With Us Or Against Us.
Is this our future? Hooked up to AI to create a swarm intelligence? And maybe the bigger question is why? Because if you still think this is about money, you're wrong. Sorry, but you are. And even if you don't agree, don't come back in a year or two and say, I told you so. Why? Because this is a long game and if you want to understand better, look into game theory. There will be an upcoming post on this later.
So do we have a
CLEARVIEW on
AI yet?
Clearview AI cancels contract with RCMP, says it’s no longer offering its facial recognition tech in Canada. Clearview AI claims its solution has an advantage over competing facial recognition products because it has copied more than 3 billion images from the internet, including from social media platforms like Facebook, Instagram, Twitter and YouTube. Police forces use Clearview AI to compare images of unknown people — usually suspects — to this database for identification. In addition to questions about the legality of photo scraping for a commercial entity, Clearview AI apparently keeps images in its database even if someone deletes their image from a website. Source Here
Now here's the crux of the problem. Do they see all of this as necessary for the war over natural resources that seems to get closer everyday? Is the environment worse (as I suspect it is in this series) than they've told us, and do they think that we can't make it past without linking up to AI? And here's another thought, maybe AI can't work without us being hooked up to it? Because make no mistake about anybody associated with AI, they are all part of the same group. Here's looking at you Elon. I see you. Good cop. Bad cop. Problem. Reaction. Solution.
And what about MAGA? I kind of brought it up and left it hanging. Let's cut down the noose, shall we. Now remember, words matter. All words. Nothing is an accident at this point. Maybe humans couldn't come up with this intricate of a plan, but a supercomputer could have. Because I don't believe what I'm about to write is an accident.
MAGA • Make America Great Again.
MAGA • Microsoft Apple Google Amazon.
Trump opened the meeting with CEOs from **Google, Microsoft, Apple, Amazon and others by thanking Thiel for his support. “I want to add that I am here to help you folks do well. And you’re doing well right now and I’m very honored by the bounce. They’re all talking about the bounce. So right now everybody in this room has to like me, at least a little bit,” Trump said, perhaps in reference to the fact that he received little support from Silicon Valley during his campaign.
Doesn't that sound like Big Tech got him there? Thank you for the bounce? Really. And why did they thank Thiel for his support? Doesn't it seem off? Or is it just me? Am I suffering another delusional conspiracy moment? Is this apophenia? They are all connected to government after all. And government is putting it's full weight behind AI.
On February 11, 2019, President Trump signed Executive Order 13859 announcing the American AI Initiative — the United States’ national strategy on artificial intelligence. This strategy is a concerted effort to promote and protect national AI technology and innovation. The Initiative implements a whole-of-government strategy in collaboration and engagement with the private sector, academia, the public, and like-minded international partners.
Heck, every alliance country is on-board to be online for AI.
Canada and a dozen other countries have launched the Global Partnership on Artificial Intelligence (GPAI), Prime Minister Justin Trudeau announced on Monday. “Today, as one of 13 founding members, Canada helped launch the Global Partnership on Artificial Intelligence,” Trudeau told reporters. In addition to Canada, the partnership includes Australia, France, Germany, Italy, Japan, Mexico, New Zealand, South Korea, Singapore, the United Kingdom and the United States. Source Here
So we have all signed up to a Global Partnership on Artificial Intelligence. Ok. And now they're instituting health passes to be cleared for travel. OK. Now you don't think people will be going over all that health data, do you? And if you set off alarms? Then it'll be a good thing that a facial recognition AI will have a Clearview of where you've been and who you've infected. Just in case you don't have your phone. And you're wearing a mask. But is all this prep for the the future society so they can safeguard the AI system, or is it for something else? Because they're adamant that we're entering the age of pandemics, aren't they? And we're getting pretty antagonistic towards the east, aren't we? Or they are towards us, depending on your POV.
So is the health pass because of environmental collapse? Or is it to guard against bioweapons? Or is it both, with the added bonus of setting up a failsafe AI World Government? Remember, Gray War and Unrestricted Warfare tactics are all about keeping the enemy unbalanced. And so far, so good. And I hope that you're ready to settle in for the long haul, because U/pinkpolkagirl has it right with this post •
MKULTRA So just remember, we're all in this
Not Normal New Normal together.
Heads up and eyes open. Talk again soon.
So, if you're like me, you're pretty sad that the NBA season is over. It was a great season, with lots of money made, and now that it's gone you feel bored, and want to find new ways to increase the bankroll. And if you're like me, one of your first thoughts was to look at those world basketball games on your sportsbook, and to wonder if you could maybe try out your NBA strategies on world basketball games...
...and if you're like me, you probably lost a fair bit before you finally got fed up, said fuck the Lithuanian LMKL, and went back to waiting for NBA to start again.
But if you're like me, you still have an itching feeling that there's money to be made there, if only you could figure out how.
At first, the difficulty with world basketball seems to be a straightforward problem:
You don't have a good model for world basketball leagues.
But this has an easy enough solution, right? Just build a world basketball model! So that's what I did. (Not before losing another $100 on the Lithuanian LMKL, but let's not talk about that.) Over a week and a half, I collected a shitton of data on world basketball games and world basketball leagues and built a world basketball model, no NBA involved, and then let it ride.
Unfortunately, this led to the discovery of a second problem:
World basketball leagues are all very different.
A single world basketball model sucks, in other words. Any model of the full set of world leagues necessarily takes an average of extremely different statistical relationships, and as we know, there's no such thing as the average person, and no such thing as the average basketball league. Which means that this model was wrong, in one way or another, for almost every league I tried it on. (FUCKING LITHUANIAN LMKL.)
Obviously, the first-best solution is to have a model for every single basketball league around the world. You get enough data, then you build a model on every single league you can find.
But this leads, then, to the third and hardest problem:
There's not enough data to build a world basketball model for every single league.
Maybe this isn't true for everyone, but it's true for me. I've collected extensive data on hundreds of games and almost 60 leagues over the last few weeks, but the median number of games for a given league is still in the low single digits in my data, because there are just so many leagues out there, and data sources aren't great for all of them, at least as far as I've found. (Which is to say: if you know where to get good historical data on world leagues, hit me up!)
There's an inescapable tradeoff that you face, then, between sample size and goodness of fit: either you use a lot of games that aren't similar, to get a large enough sample size to build a robust model, or you use incredibly small samples to build 60ish models that are appropriate to each league, but are awful models due to the terrible ratio of signal to noise.
So what do we do?
A non-degenerate would say, "stop fucking gambling on Lithuanian basketball," I suppose.
But, being a degenerate, I say,
Develop an iterative clustering algorithm for optimally grouping world basketball leagues, build a separate model for each cluster, and then share your preliminary findings with Reddit
And here we are!
These results are incredibly preliminary — I have yet to bet with them, and I DO NOT advise you to — but I recently developed an algorithm that tries to optimally trade off between sample size and goodness of fit by iteratively clustering basketball leagues, allowing the model to make use of the data from leagues that it's algorithmically inferred to be similar, but without forcing it to reconcile truly irreconcilable statistical patterns.
Now, first, a series of caveats:
- Right now, I only build models for live oveunders. So these similarities only correspond to how similar leagues are in terms of modeling live oveunder bets. They are NOT evidence of similarity in other respects.
- My sample sizes are still VERY SMALL! Most of the leagues here only have a few games in my dataset, which is why I'm using this clustering algorithm, but it's also why you shouldn't put much, if any, stock into these early-early findings. If you're going to make any inferences, broad conclusions are more likely to be valid than any specific assumptions about a particular league.
- Again, don't bet with this! I think it's interesting enough to share, but it is NOT robust enough to gamble on. (The one exception
might be the CBA; when testing my NBA model in mid-July, I previously confirmed that NBA-based models (at least, those built according to my style) can perform OK on the CBA, so you can take that how you will.)
So, without further ado, if anyone's still reading this far down — my model identified four clusters in the leagues that I have data on, grouped as follows:
| Group 1 | Group 2 | Group 3 | Group 4 |
| Portugal - LPB | Nicaragua - Nicaragua Championship | Egypt - Premier League | Russia - Superleague |
| France - LNB | International - VTB United League Youth | Uruguay - 2nd Division | International - WABA League Women |
| Spain - LEB Oro | Australia - QSL | France - Division 3 | Brazil - FPB Paulista League Men |
| Basketball - CBA | International - ABA League | Estonia - Estonia Liga 1 | Finland - Finland Division 1A |
| Italy - Italy Serie A1 Women | South Korea - KBL | Czech Republic - NBL | Hungary - Hungary NB I. A Women |
| Finland - Korisliiga W | Mexico - LNBP | International - Club Friendlies | Philippines - PBA Philippine Cup |
| Sweden - Sweden Basketligan Women | Basketball - Eurocup | France - Pro B | |
| Japan - WJBL Women | Turkey - Turkey Super Ligi | France - Coupe de France | |
| Bulgaria - NBL | Japan - Japan B League | Denmark - Denmark Basketligaen | |
| Czech Republic - ZBL W | Nicaragua - Championship | Turkey - Federation Cup | |
| | France - LFB Women | Lithuania - LKL | |
| | Russia - Premier League Women | Basketball - Euroleague | |
| | Italy - Serie A | Poland - Energa Basket Liga | |
| | Czech Republic - 1. Liga | Brazil - Brazil Paulista Women | |
| | Turkey - Super Lig Women | | |
| | Croatia - A1 Liga | | |
| | Chinese Taipei - Super Basketball League | | |
| | Poland - 1 Liga | | |
| | Russia - Russian Cup | | |
| | Lithuania - LMKL | | |
| | Portugal - Portuguese Cup | | |
| | Netherlands - DBL | | |
| | Spain - Liga Endesa | | |
| | Japan - Japan B League Division 2 | | |
| | | | |
| Some random statistics: | | | | |
| Goodness-of-fit (i.e. how easy is it for my model to explain live O/U for these leagues?) | 48.675 | 45.663 | 44.836 | 44.897 |
| Distance from this cluster's model and my NBA model (i.e. how different are these leagues from the NBA, according to one simple measure?) | 0.0370 | 0.0851 | 0.4708 | 0.1980 |
| Would you recommend betting on this league? | No. Maybe eventually, but no. | Definitely not. | No. | No. |
Now, there aren't really that many takeaways here; mainly, this is the beginning of a process, and I expect both the process to improve as I work on it more and as I collect more data. But we can start to make out some glimmers of how to bet on world basketball in the future from here: the leftmost column suggests that there do exist a small subset of world leagues where basketball is more similar to the NBA, and that these leagues are easier to model, even though this isn't enough evidence to show that these particular leagues are the "good ones" or not. Also, gender appears to matter a lot in groupings. Women's leagues are far more common in groups 1 and 2, which my model finds to be far more predictable, and group 3, the hardest group of leagues to model, is almost entirely men's leagues. That group is also the most dissimilar to the NBA, apparently. Finally, it doesn't seem like nationality matters that much; we see a lot of leagues from the same country in rather different groups.
But a LOT of these categorizations are because I just have extremely limited data on these leagues, and the games that I do have just happened to be similar in certain ways that my model likes. So even these limited conclusions may change.
If you guys are interested, I'd be happy to update this more in later weeks, as I collect more data and get better estimates of how to cluster world basketball games. And regardless, for those of you who made it this far down, thanks for reading!
-Abe
japan j league japan j league predictions and statistical data, H2H, Current Form, Squads, etc. See for yourself! japan j league free football predictions and tips, statistics, odds comparison and match previews. Football (soccer) statistics, team information, match predictions, bet tips, expert reviews, bet information.However, being able to identify such draw prospects is no guarantee that Bet Offers . Today's Tips; Tomorrow's Tips; England; Spain; Germany ; Italy; France; Home > Predictions > Japan J-League; Japan J-League Tips and Predictions. View our Japan J-League tips for the next games below. Select any game to view our detailed analysis on each game. Match Tips & Odds BTTS Tips & Odds Over/Under 2.5 Tips & Odds PredictZ vs WinDrawWin. Odds displayed on this page are J1 League Predictions & Betting Tips Japan. Our J League predictions & betting tips are all here, along with the latest league table and up-to-date statistics. Select a match to see more in-depth J League game predictions & team stats. Sagan Tosu . Oita Trinita. 2020-12-19 05:00:00 2 - 2 👉 Sagan Tosu to win . Preview & Prediction » Gamba Osaka. Shimizu S-Pulse. 2020-12-19 05:00:00 0 - 2 Japan J-League Prediction, H2H, Tip and Match Preview. BetClan Accumulator Tips for Today, Daily Acca's, Acca Tips and Predictions for Today's and Weekend's Football, Tennis, Basketball and Cricket events., A lot of fans and analyst always give a prediction about who is going to win the match before it starts and Prediction is done by calculating a number of variables, such as home advantage England League One 22 England League Two 21 England Championship 21 English Premier League 11 England FA Cup 9 EFL Trophy 2. Europe. Champions League 6. France. Coupe de France 15 France Ligue 1 10 France Ligue 2 10. Germany. Germany 3. Liga 14 Bundesliga 2 9 Bundesliga 9. Greece. Greece Super League 7 Greek Cup 4. Hungary. Hungary NB 1 6. International. Club friendlies 2 FIFA Club World Cup 2 Japan J-League free football predictions and tips, statistics, odds comparison and match previews. Japanese League 1 predictions J1 League is the top league of Japan football. J1 League is known as Japanese League 1. In Japan J League 1 schedule of each match, we give free predictions of the Japanese first division win/draw/win, the number of the goals and the safer bet of double chance. Bet on Premier League matches. Japan J1 League Computer Soccer Predictions. Japan J1 League 1 X 2 Over Under Prediction; 26.02 10:00: Kawasaki Frontale - Yokohama F. Marinos %72 %11 %17 %55 %45: 1: 27.02 05:00: Consadole Sapporo - Yokohama %61 %27 %12 %38 %62: Under 2.5: 27.02 05:00: Urawa Reds - Tokyo %44 %16 %40 %55 %45: Over 2.5: 27.02 05:00: Sanfrecce Hiroshima - Vegalta Sendai %38 %27 %35 Japan J-League free football predictions and tips, statistics, odds comparison and match previews. Japan J-League Predictions, Tips and Game Previews - Free Japan J-League Football Betting Predictions and Statistics
Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube. Part 2: https://www.youtube.com/watch?v=lY_w8U6W0oE&t=1sIt's a miracle some of these guys even made itMusic: https://www.youtube.com/channel/UC9Mf7_YXe83CsYB... NFL Pranks THANKS SO MUCH FOR WATCHING! If you enjoyed maybe like and subscribe or not and if you didn't like it DISLIKE and UNSUBSCRIBE! ; pSUBSCRIBE-https:... live football match today is the greatest live football streaming channel on youtube we upload daily new videos everyday...Live football,football live,footba... These are the things that will never happen again! Stuff like hitting a home run with no hands or getting a save in a 30-3 game! #MLB #Baseball #WesleyAPEXMy... This is an Insanely crazy video of nearly impossible stunts, and Amazingly Awesome trickshots! Thanks for watching my video! If you enjoyed please like the v... Akafuji Slot: http://www.youtube.com/c/AKAFUJISLOTJACKPOT LIVE★Triple Double Diamond Slot Handpay on Free Play ! San Manuel Casino, Akafuji slot https://www... I mean, I HAD to make a video about a bunch of YouTubers fighting in a hungergames like battle for the chance to win 33k, right? $200,000 Youtuber Battle Roy... Follow us on INSTAGRAM: https://www.instagram.com/jubileemedia/Bonnie: https://www.instagram.com/itsbonnieblack/Ien: https://www.instagram.com/ienthekorean/S... Sportsbook Review is a community of online bettors that strive for a win every single day. How do we help our community? We gather the industry influencers and put them at everyone's reach via our ...
